{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基于信贷数据进行用户信贷分类，使用Logistic算法和KNN算法构建模型，并比较这两大类算法的效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import sklearn\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.linear_model.coordinate_descent import ConvergenceWarning\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## 设置字符集，防止中文乱码\n",
    "mpl.rcParams['font.sans-serif']=[u'simHei']\n",
    "mpl.rcParams['axes.unicode_minus']=False\n",
    "## 拦截异常\n",
    "warnings.filterwarnings(action = 'ignore', category=ConvergenceWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据条数: 690\n",
      "过滤后数据条数: 653\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "      <th>A16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.25</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00202</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>3.04</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00043</td>\n",
       "      <td>560</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>1.50</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00280</td>\n",
       "      <td>824</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>00100</td>\n",
       "      <td>3</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>00120</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  A1     A2     A3 A4 A5 A6 A7    A8 A9 A10  A11 A12 A13    A14  A15 A16\n",
       "0  b  30.83  0.000  u  g  w  v  1.25  t   t    1   f   g  00202    0   +\n",
       "1  a  58.67  4.460  u  g  q  h  3.04  t   t    6   f   g  00043  560   +\n",
       "2  a  24.50  0.500  u  g  q  h  1.50  t   f    0   f   g  00280  824   +\n",
       "3  b  27.83  1.540  u  g  w  v  3.75  t   t    5   t   g  00100    3   +\n",
       "4  b  20.17  5.625  u  g  w  v  1.71  t   f    0   f   s  00120    0   +"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 加载数据并对数据进行预处理\n",
    "# 1. 加载数据\n",
    "path = \"datas/crx.data\"\n",
    "names = ['A1','A2','A3','A4','A5','A6','A7','A8',\n",
    "         'A9','A10','A11','A12','A13','A14','A15','A16']\n",
    "df = pd.read_csv(path, header=None, names=names)\n",
    "print(\"数据条数:\", len(df))\n",
    "\n",
    "# 2. 异常数据过滤\n",
    "df = df.replace(\"?\", np.nan).dropna(how='any')\n",
    "print(\"过滤后数据条数:\", len(df))\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def parse(v, l):\n",
    "    return [1 if i == v else 0 for i in l]\n",
    "def parseRecord(record):\n",
    "    result = []\n",
    "    ## 格式化数据，将离线数据转换为连续数据\n",
    "    a1 = record['A1']\n",
    "    for i in parse(a1, ('a', 'b')):\n",
    "        result.append(i)\n",
    "    \n",
    "    result.append(float(record['A2']))\n",
    "    result.append(float(record['A3']))\n",
    "    \n",
    "    \n",
    "    a4 = record['A4']\n",
    "    for i in parse(a4, ('u', 'y', 'l', 't')):\n",
    "        result.append(i)\n",
    "    \n",
    "    a5 = record['A5']\n",
    "    for i in parse(a5, ('g', 'p', 'gg')):\n",
    "        result.append(i)\n",
    "    \n",
    "    a6 = record['A6']\n",
    "    for i in parse(a6, ('c', 'd', 'cc', 'i', 'j', 'k', 'm', 'r', 'q', 'w', 'x', 'e', 'aa', 'ff')):\n",
    "        result.append(i)\n",
    "    \n",
    "    a7 = record['A7']\n",
    "    for i in parse(a7, ('v', 'h', 'bb', 'j', 'n', 'z', 'dd', 'ff', 'o')):\n",
    "        result.append(i)\n",
    "    \n",
    "    result.append(float(record['A8']))\n",
    "    \n",
    "    a9 = record['A9']\n",
    "    for i in parse(a9, ('t', 'f')):\n",
    "        result.append(i)\n",
    "        \n",
    "    a10 = record['A10']\n",
    "    for i in parse(a10, ('t', 'f')):\n",
    "        result.append(i)\n",
    "    \n",
    "    result.append(float(record['A11']))\n",
    "    \n",
    "    a12 = record['A12']\n",
    "    for i in parse(a12, ('t', 'f')):\n",
    "        result.append(i)\n",
    "        \n",
    "    a13 = record['A13']\n",
    "    for i in parse(a13, ('g', 'p', 's')):\n",
    "        result.append(i)\n",
    "    \n",
    "    result.append(float(record['A14']))\n",
    "    result.append(float(record['A15']))\n",
    "    \n",
    "    a16 = record['A16']\n",
    "    if a16 == '+':\n",
    "        result.append(1)\n",
    "    else:\n",
    "        result.append(0)\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1_0</th>\n",
       "      <th>A1_1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4_0</th>\n",
       "      <th>A4_1</th>\n",
       "      <th>A4_2</th>\n",
       "      <th>A4_3</th>\n",
       "      <th>A5_0</th>\n",
       "      <th>A5_1</th>\n",
       "      <th>...</th>\n",
       "      <th>A10_1</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12_0</th>\n",
       "      <th>A12_1</th>\n",
       "      <th>A13_0</th>\n",
       "      <th>A13_1</th>\n",
       "      <th>A13_2</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "      <th>A16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>824.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   A1_0  A1_1     A2     A3  A4_0  A4_1  A4_2  A4_3  A5_0  A5_1 ...   A10_1  \\\n",
       "0   0.0   1.0  30.83  0.000   1.0   0.0   0.0   0.0   1.0   0.0 ...     0.0   \n",
       "1   1.0   0.0  58.67  4.460   1.0   0.0   0.0   0.0   1.0   0.0 ...     0.0   \n",
       "2   1.0   0.0  24.50  0.500   1.0   0.0   0.0   0.0   1.0   0.0 ...     1.0   \n",
       "3   0.0   1.0  27.83  1.540   1.0   0.0   0.0   0.0   1.0   0.0 ...     0.0   \n",
       "4   0.0   1.0  20.17  5.625   1.0   0.0   0.0   0.0   1.0   0.0 ...     1.0   \n",
       "\n",
       "   A11  A12_0  A12_1  A13_0  A13_1  A13_2    A14    A15  A16  \n",
       "0  1.0    0.0    1.0    1.0    0.0    0.0  202.0    0.0  1.0  \n",
       "1  6.0    0.0    1.0    1.0    0.0    0.0   43.0  560.0  1.0  \n",
       "2  0.0    0.0    1.0    1.0    0.0    0.0  280.0  824.0  1.0  \n",
       "3  5.0    1.0    0.0    1.0    0.0    0.0  100.0    3.0  1.0  \n",
       "4  0.0    0.0    1.0    0.0    0.0    1.0  120.0    0.0  1.0  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 数据特征处理(将数据转换为数值类型的)\n",
    "new_names =  ['A1_0', 'A1_1',\n",
    "              'A2','A3',\n",
    "              'A4_0','A4_1','A4_2','A4_3',\n",
    "              'A5_0', 'A5_1', 'A5_2', \n",
    "              'A6_0', 'A6_1', 'A6_2', 'A6_3', 'A6_4', 'A6_5', 'A6_6', 'A6_7', 'A6_8', 'A6_9', 'A6_10', 'A6_11', 'A6_12', 'A6_13', \n",
    "              'A7_0', 'A7_1', 'A7_2', 'A7_3', 'A7_4', 'A7_5', 'A7_6', 'A7_7', 'A7_8', \n",
    "              'A8',\n",
    "              'A9_0', 'A9_1' ,\n",
    "              'A10_0', 'A10_1',\n",
    "              'A11',\n",
    "              'A12_0', 'A12_1',\n",
    "              'A13_0', 'A13_1', 'A13_2',\n",
    "              'A14','A15','A16']\n",
    "datas = df.apply(lambda x: pd.Series(parseRecord(x), index = new_names), axis=1)\n",
    "names = new_names\n",
    "\n",
    "## 展示一下处理后的数据\n",
    "datas.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## 数据分割\n",
    "X = datas[names[0:-1]]\n",
    "Y = datas[names[-1]]\n",
    "\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.1,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## 数据正则化操作(归一化)\n",
    "ss = StandardScaler()\n",
    "X_train = ss.fit_transform(X_train) ## 训练正则化模型，并将训练数据归一化操作\n",
    "X_test = ss.fit_transform(X_test) ## 使用训练好的模型对测试数据进行归一化操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=array([  1.00000e-04,   1.26486e-04,   1.59986e-04,   2.02359e-04,\n",
       "         2.55955e-04,   3.23746e-04,   4.09492e-04,   5.17947e-04,\n",
       "         6.55129e-04,   8.28643e-04,   1.04811e-03,   1.32571e-03,\n",
       "         1.67683e-03,   2.12095e-03,   2.68270e-03,   3.39322e-03,\n",
       "         4.29193e-03,   5.428...     3.08884e+00,   3.90694e+00,   4.94171e+00,   6.25055e+00,\n",
       "         7.90604e+00,   1.00000e+01]),\n",
       "           class_weight=None, cv=None, dual=False, fit_intercept=True,\n",
       "           intercept_scaling=1.0, max_iter=100, multi_class='ovr',\n",
       "           n_jobs=1, penalty='l2', random_state=None, refit=True,\n",
       "           scoring=None, solver='lbfgs', tol=0.01, verbose=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Logistic算法模型构建\n",
    "lr = LogisticRegressionCV(Cs=np.logspace(-4,1,50), fit_intercept=True, penalty='l2', solver='lbfgs', tol=0.01, multi_class='ovr')\n",
    "lr.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic算法R值（准确率）： 0.890971039182\n",
      "Logistic算法稀疏化特征比率：2.13%\n",
      "Logistic算法参数： [[-0.00507672  0.00507672  0.06367298  0.06284643  0.03997945 -0.04935683\n",
      "   0.06679464  0.          0.03997945 -0.04935683  0.06679464  0.00549457\n",
      "  -0.02646873  0.1057865  -0.10294239 -0.02346199 -0.05981958 -0.0131902\n",
      "   0.01148842  0.04690591  0.03631018  0.13996153  0.03858644 -0.02422956\n",
      "  -0.11817039 -0.00441403  0.08130739 -0.02489682  0.03130081  0.03567533\n",
      "  -0.01396069 -0.00769375 -0.10417126 -0.00379776  0.15834772  0.46892613\n",
      "  -0.46892613  0.16546747 -0.16546747  0.19117654 -0.01273762  0.01273762\n",
      "   0.01240825 -0.00744574 -0.0110668  -0.08907636  0.12989149]]\n",
      "Logistic算法截距： [-0.25992008]\n"
     ]
    }
   ],
   "source": [
    "## Logistic算法效果输出\n",
    "lr_r = lr.score(X_train, Y_train)\n",
    "print(\"Logistic算法R值（准确率）：\", lr_r)\n",
    "print(\"Logistic算法稀疏化特征比率：%.2f%%\" % (np.mean(lr.coef_.ravel() == 0) * 100))\n",
    "print(\"Logistic算法参数：\",lr.coef_)\n",
    "print(\"Logistic算法截距：\",lr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Logistic算法预测\n",
    "lr_y_predict = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='kd_tree', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n",
       "           weights='distance')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## KNN算法构建\n",
    "knn = KNeighborsClassifier(n_neighbors=3, algorithm='kd_tree', weights='distance')\n",
    "knn.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic算法R值（准确率）： 1.0\n"
     ]
    }
   ],
   "source": [
    "## KNN算法效果输出\n",
    "knn_r = knn.score(X_train, Y_train)\n",
    "print(\"Logistic算法R值（准确率）：\", knn_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## KNN算法预测\n",
    "knn_y_predict = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KJRITE6FUKtGgQQOt5VJTUxEfH48xY8bA19dXnt66dWtIkiQHIpKSkgAA3bt3R3JyMuLj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\nTs/169dHUFCQwWWuAHgYC/hfBoqtAZAEpJUHYpX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sQW/y14XOOx3vdUuFcBny35rMjrTBnbEI0bNwYAHDt2TNra9scffyA+Ph4+Pj4Achc5nDt3DpaW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\nRKWoWImkCRMmIDg4GADg5+eHJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x4dc9550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 结果图像展示\n",
    "## c. 图表展示\n",
    "x_len = range(len(X_test))\n",
    "plt.figure(figsize=(14,7), facecolor='w')\n",
    "plt.ylim(-0.1,1.1)\n",
    "plt.plot(x_len, Y_test, 'ro',markersize = 6, zorder=3, label=u'真实值')\n",
    "plt.plot(x_len, lr_y_predict, 'go', markersize = 10, zorder=2, label=u'Logis算法预测值,$R^2$=%.3f' % lr.score(X_test, Y_test))\n",
    "plt.plot(x_len, knn_y_predict, 'yo', markersize = 16, zorder=1, label=u'KNN算法预测值,$R^2$=%.3f' % knn.score(X_test, Y_test))\n",
    "plt.legend(loc = 'center right')\n",
    "plt.xlabel(u'数据编号', fontsize=18)\n",
    "plt.ylabel(u'是否审批(0表示通过，1表示通过)', fontsize=18)\n",
    "plt.title(u'Logistic回归算法和KNN算法对数据进行分类比较', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
